Quantitative Method for Security Situation of the Power Information Network Based on the Evolutionary Neural Network

TL;DR

This paper introduces a novel method for quantifying the security situation of power information networks using an evolutionary neural network, integrating business impact, reliability, threats, and vulnerabilities for comprehensive risk assessment.

Contribution

It develops a new quantitative model based on an evolutionary neural network that considers power application services and network interconnections, improving security assessment accuracy.

Findings

The proposed method effectively reflects the security risk state of power networks.

Simulation results verify the model's effectiveness and robustness.

The approach integrates business impact into security quantification.

Abstract

Cybersecurity is the security cornerstone of digital transformation of the power grid and construction of new power systems. The traditional network security situation quantification method only analyzes from the perspective of network performance, ignoring the impact of various power application services on the security situation, so the quantification results cannot fully reflect the power information network risk state. This study proposes a method for quantifying security situation of the power information network based on the evolutionary neural network. First, the security posture system architecture is designed by analyzing the business characteristics of power information network applications. Second, combining the importance of power application business, the spatial element index system of coupled interconnection is established from three dimensions of network reliability, threat, and vulnerability. Then, the BP neural network optimized by the genetic evolutionary algorithm is incorporated into the element index calculation process, and the quantitative model of security posture of the power information network based on the evolutionary neural network is constructed. Finally, a simulation experiment environment is built according to a power sector network topology, and the effectiveness and robustness of the method proposed in the study are verified.